**2. Problem Formulations**

Our purpose is to forecast future power consumption using self-historical data. The self-historical data of power consumption could be expressed as a time series as follows:

$$T = (t\_{1\prime} \ t\_{2\prime} \ t\_{3\prime} \ \dots \ t\_{i\prime} \ \dots \ t\_{l\prime}) \tag{1}$$

where *T* contains *N* data points. Different types of forecasts have different elements in *T*. We defined four types of forecasts as shown in Table 1 and described follows.


**Table 1.** Defined four types of forecasts.


The time series *T* needs to reconstruct as Equation (2) to satisfy the input of the proposed deep model. The input matrix includes *N* − *L* samples; the length of sample *x*(*t*) is *L*. Different types of forecasts have different *L*, which corresponds to *H*, *D*, *W*, and *M*. The corresponding output is defined as Equation (3). Every output is the electricity consumption of the next duration.

$$Input = \begin{bmatrix} t\_1 & t\_2 & t\_3 & \dots & t\_{L-1} & t\_L \\ t\_2 & t\_3 & t\_4 & \dots & t\_L & t\_{L+1} \\ \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\ t\_{N-L} & t\_{N-L+1} & t\_{N-L+2} & \dots & t\_{N-2} & t\_{N-1} \end{bmatrix} \tag{2}$$

$$Output = \begin{bmatrix} t\_{L+1} \\ t\_{L+2} \\ \vdots \\ \vdots \\ t\_N \end{bmatrix} \tag{3}$$
